{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pylab as plt\n",
    "%matplotlib inline\n",
    "from matplotlib.pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 6\n",
    "from datetime import datetime\n",
    "from statsmodels.tsa.stattools import acf  \n",
    "from statsmodels.tsa.stattools import pacf\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stem/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (4,61,62,66,116,117,123) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "data = pd.read_csv('Ter2.csv',encoding=\"latin-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df=data['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    16840\n",
       "2015    14806\n",
       "2013    11990\n",
       "2012     8498\n",
       "1992     5073\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv('final.csv', sep=',', encoding='utf-8')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dateparse = lambda dates: pd.datetime.strptime(dates, '%Y')\n",
    "data = pd.read_csv('final.csv',encoding=\"utf-8\", parse_dates=['year'], index_col='year',date_parser=dateparse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1970-01-01', '1971-01-01', '1972-01-01', '1973-01-01',\n",
       "               '1974-01-01', '1975-01-01', '1976-01-01', '1977-01-01',\n",
       "               '1978-01-01', '1979-01-01', '1980-01-01', '1981-01-01',\n",
       "               '1982-01-01', '1983-01-01', '1984-01-01', '1985-01-01',\n",
       "               '1986-01-01', '1987-01-01', '1988-01-01', '1989-01-01',\n",
       "               '1990-01-01', '1991-01-01', '1992-01-01', '1994-01-01',\n",
       "               '1995-01-01', '1996-01-01', '1997-01-01', '1998-01-01',\n",
       "               '1999-01-01', '2000-01-01', '2001-01-01', '2002-01-01',\n",
       "               '2003-01-01', '2004-01-01', '2005-01-01', '2006-01-01',\n",
       "               '2007-01-01', '2008-01-01', '2009-01-01', '2010-01-01',\n",
       "               '2011-01-01', '2012-01-01', '2013-01-01', '2014-01-01',\n",
       "               '2015-01-01'],\n",
       "              dtype='datetime64[ns]', name=u'year', freq=None)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ts=data['count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1970-01-01     651\n",
       "1971-01-01     470\n",
       "1972-01-01     492\n",
       "1973-01-01     472\n",
       "1974-01-01     577\n",
       "1975-01-01     739\n",
       "1976-01-01     921\n",
       "1977-01-01    1314\n",
       "1978-01-01    1524\n",
       "1979-01-01    2658\n",
       "1980-01-01    2663\n",
       "1981-01-01    2585\n",
       "1982-01-01    2544\n",
       "1983-01-01    2870\n",
       "1984-01-01    3494\n",
       "1985-01-01    2915\n",
       "1986-01-01    2859\n",
       "1987-01-01    3184\n",
       "1988-01-01    3721\n",
       "1989-01-01    4322\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x115e3d610>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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HJ6tWJR/7WHLbbcN77vYI4gAAAAAYii1bkiuuGG4QlyRnnJHsvXfy3/7bcJ87lSAOAAAA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\nfti+i4EDQKWgX7e26I+B0L7VwO1Bv0Zt0RkDWOK1a6bH2pJ2DPYHVue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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115e3d410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(ts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Checking Stationarity of TimeSeries\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "def test_stationarity(timeseries):\n",
    "    \n",
    "    #Determing rolling statistics\n",
    "    rolmean = pd.rolling_mean(timeseries, window=12)\n",
    "    rolstd = pd.rolling_std(timeseries, window=12)\n",
    "\n",
    "    #Plot rolling statistics:\n",
    "    orig = plt.plot(timeseries, color='blue',label='Original')\n",
    "    mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show(block=False)\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print 'Results of Dickey-Fuller Test:'\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in dftest[4].items():\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print dfoutput"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).mean()\n",
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).std()\n"
     ]
    },
    {
     "data": {
      "image/png": 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+NG/enLZt2/Ld736XJUuW1HiMyvaI69SpE6eccgovvvgihxxyCC1atKBLly7c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\n4bjjjstAkdVP1oK4EEID4CngfGB2pZu7AjcmSfJykiQfAWcRg7YTSj9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cMYOXJkdTn99NMDvqCHyInSYg0BpiBOwtnBgzbU9KmnYONG6N3ber5ddx306hXq2omI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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1284bc690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  1.234002\n",
      "p-value                         0.996213\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    44.000000\n",
      "Critical Value (5%)            -2.929886\n",
      "Critical Value (1%)            -3.588573\n",
      "Critical Value (10%)           -2.603185\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "test_stationarity(ts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though standard deviation is small, mean is clearly varying with time and this is not a stationary series. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).mean()\n",
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).std()\n"
     ]
    },
    {
     "data": {
      "image/png": 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77s0338yRI0eoWrVqth3jZCIjI5k/f36K9W+++WZYwzsRERERERGRU+G9jSfW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115ea4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -0.699575\n",
      "p-value                         0.846859\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    44.000000\n",
      "Critical Value (5%)            -2.929886\n",
      "Critical Value (1%)            -3.588573\n",
      "Critical Value (10%)           -2.603185\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Log Transformation\n",
    "data.log= data.apply(lambda x: np.log(x))  \n",
    "test_stationarity(data.log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.fd=data-data.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).mean()\n",
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).std()\n"
     ]
    },
    {
     "data": {
      "image/png": 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b8Y899thqk2kTJ07k+OOPJ6XUADOTJEmSJKlpe/ddePXVjV/iCVkybenSup/T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\nswxtAEDVIjUl16yp+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x122d0fcd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -4.655268\n",
      "p-value                         0.000102\n",
      "#Lags Used                      0.000000\n",
      "Number of Observations Used    43.000000\n",
      "Critical Value (5%)            -2.931550\n",
      "Critical Value (1%)            -3.592504\n",
      "Critical Value (10%)           -2.604066\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#First_Difference\n",
    "ts1=data['fd']  \n",
    "test_stationarity(ts1.dropna(inplace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data\n",
    "data.sd=data-data.shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).mean()\n",
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).std()\n"
     ]
    },
    {
     "data": {
      "image/png": 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Hj+Y73/kOt9xyCyeddFK12Ntvv51OnTpx1VVXcfPNN9OhQwfOO+88xowZU6d7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\n7mLZsmWNoqDluylJkiRJ+iq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0a9YsTZ48WfPmzVPJkiXVq1cvPfvs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O6/H26/Hf72t6hSu/TSWIS+vZgzBx55BG68ET74oNq9aT8++gi++tVYU+/3v4du3ardI0nquLpU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\n/FyiDH4yH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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12b045b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -4.086747\n",
      "p-value                         0.001019\n",
      "#Lags Used                      9.000000\n",
      "Number of Observations Used    23.000000\n",
      "Critical Value (5%)            -2.998500\n",
      "Critical Value (1%)            -3.752928\n",
      "Critical Value (10%)           -2.638967\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "ts2=data['sd']  \n",
    "test_stationarity(ts2.dropna(inplace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data.sfd=ts1-ts1.shift(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).mean()\n",
      "/Users/stem/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).std()\n"
     ]
    },
    {
     "data": {
      "image/png": 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HHMHtt99Ou3btGDFiBHPmzKns9/7773PooYcyZ84crrzySi655BImTpzIHXfc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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11da2cad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                 -1.478647\n",
      "p-value                         0.544047\n",
      "#Lags Used                      9.000000\n",
      "Number of Observations Used    22.000000\n",
      "Critical Value (5%)            -3.005426\n",
      "Critical Value (1%)            -3.769733\n",
      "Critical Value (10%)           -2.642501\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#seasonal_first_difference\n",
    "ts3=data['sfd']  \n",
    "test_stationarity(ts3.dropna(inplace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>log</th>\n",
       "      <th>fd</th>\n",
       "      <th>sd</th>\n",
       "      <th>sfd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>651</td>\n",
       "      <td>6.478510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>470</td>\n",
       "      <td>6.152733</td>\n",
       "      <td>-181.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>492</td>\n",
       "      <td>6.198479</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>472</td>\n",
       "      <td>6.156979</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>577</td>\n",
       "      <td>6.357842</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>739</td>\n",
       "      <td>6.605298</td>\n",
       "      <td>162.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>921</td>\n",
       "      <td>6.825460</td>\n",
       "      <td>182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-01</th>\n",
       "      <td>1314</td>\n",
       "      <td>7.180831</td>\n",
       "      <td>393.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01-01</th>\n",
       "      <td>1524</td>\n",
       "      <td>7.329094</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01</th>\n",
       "      <td>2658</td>\n",
       "      <td>7.885329</td>\n",
       "      <td>1134.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>2663</td>\n",
       "      <td>7.887209</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>2585</td>\n",
       "      <td>7.857481</td>\n",
       "      <td>-78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>2544</td>\n",
       "      <td>7.841493</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>2870</td>\n",
       "      <td>7.962067</td>\n",
       "      <td>326.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>507.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-01</th>\n",
       "      <td>3494</td>\n",
       "      <td>8.158802</td>\n",
       "      <td>624.0</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>602.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>2915</td>\n",
       "      <td>7.977625</td>\n",
       "      <td>-579.0</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>-559.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>2859</td>\n",
       "      <td>7.958227</td>\n",
       "      <td>-56.0</td>\n",
       "      <td>2282.0</td>\n",
       "      <td>-161.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>3184</td>\n",
       "      <td>8.065894</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2445.0</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>3721</td>\n",
       "      <td>8.221748</td>\n",
       "      <td>537.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>4322</td>\n",
       "      <td>8.371474</td>\n",
       "      <td>601.0</td>\n",
       "      <td>3008.0</td>\n",
       "      <td>208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>3887</td>\n",
       "      <td>8.265393</td>\n",
       "      <td>-435.0</td>\n",
       "      <td>2363.0</td>\n",
       "      <td>-645.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>4683</td>\n",
       "      <td>8.451694</td>\n",
       "      <td>796.0</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>-338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>5073</td>\n",
       "      <td>8.531688</td>\n",
       "      <td>390.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>385.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>3458</td>\n",
       "      <td>8.148446</td>\n",
       "      <td>-1615.0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>-1537.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>3081</td>\n",
       "      <td>8.033009</td>\n",
       "      <td>-377.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>-336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>3056</td>\n",
       "      <td>8.024862</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>-351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>3200</td>\n",
       "      <td>8.070906</td>\n",
       "      <td>144.0</td>\n",
       "      <td>-294.0</td>\n",
       "      <td>-480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>933</td>\n",
       "      <td>6.838405</td>\n",
       "      <td>-2267.0</td>\n",
       "      <td>-1982.0</td>\n",
       "      <td>-1688.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>1396</td>\n",
       "      <td>7.241366</td>\n",
       "      <td>463.0</td>\n",
       "      <td>-1463.0</td>\n",
       "      <td>519.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>1813</td>\n",
       "      <td>7.502738</td>\n",
       "      <td>417.0</td>\n",
       "      <td>-1371.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>1908</td>\n",
       "      <td>7.553811</td>\n",
       "      <td>95.0</td>\n",
       "      <td>-1813.0</td>\n",
       "      <td>-442.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>1332</td>\n",
       "      <td>7.194437</td>\n",
       "      <td>-576.0</td>\n",
       "      <td>-2990.0</td>\n",
       "      <td>-1177.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>1262</td>\n",
       "      <td>7.140453</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>-2625.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>1161</td>\n",
       "      <td>7.057037</td>\n",
       "      <td>-101.0</td>\n",
       "      <td>-3522.0</td>\n",
       "      <td>-897.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>2011</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-3062.0</td>\n",
       "      <td>460.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>2751</td>\n",
       "      <td>7.919720</td>\n",
       "      <td>740.0</td>\n",
       "      <td>-707.0</td>\n",
       "      <td>2355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>3241</td>\n",
       "      <td>8.083637</td>\n",
       "      <td>490.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>4787</td>\n",
       "      <td>8.473659</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>4721</td>\n",
       "      <td>8.459776</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>-210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>4821</td>\n",
       "      <td>8.480737</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3888.0</td>\n",
       "      <td>2367.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>5067</td>\n",
       "      <td>8.530504</td>\n",
       "      <td>246.0</td>\n",
       "      <td>3671.0</td>\n",
       "      <td>-217.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>8498</td>\n",
       "      <td>9.047586</td>\n",
       "      <td>3431.0</td>\n",
       "      <td>6685.0</td>\n",
       "      <td>3014.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>11990</td>\n",
       "      <td>9.391828</td>\n",
       "      <td>3492.0</td>\n",
       "      <td>10082.0</td>\n",
       "      <td>3397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>16840</td>\n",
       "      <td>9.731512</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>15508.0</td>\n",
       "      <td>5426.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>14806</td>\n",
       "      <td>9.602788</td>\n",
       "      <td>-2034.0</td>\n",
       "      <td>13544.0</td>\n",
       "      <td>-1964.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count       log      fd       sd     sfd\n",
       "year                                                \n",
       "1970-01-01    651  6.478510     NaN      NaN     NaN\n",
       "1971-01-01    470  6.152733  -181.0      NaN     NaN\n",
       "1972-01-01    492  6.198479    22.0      NaN     NaN\n",
       "1973-01-01    472  6.156979   -20.0      NaN     NaN\n",
       "1974-01-01    577  6.357842   105.0      NaN     NaN\n",
       "1975-01-01    739  6.605298   162.0      NaN     NaN\n",
       "1976-01-01    921  6.825460   182.0      NaN     NaN\n",
       "1977-01-01   1314  7.180831   393.0      NaN     NaN\n",
       "1978-01-01   1524  7.329094   210.0      NaN     NaN\n",
       "1979-01-01   2658  7.885329  1134.0      NaN     NaN\n",
       "1980-01-01   2663  7.887209     5.0      NaN     NaN\n",
       "1981-01-01   2585  7.857481   -78.0      NaN     NaN\n",
       "1982-01-01   2544  7.841493   -41.0   1893.0     NaN\n",
       "1983-01-01   2870  7.962067   326.0   2400.0   507.0\n",
       "1984-01-01   3494  8.158802   624.0   3002.0   602.0\n",
       "1985-01-01   2915  7.977625  -579.0   2443.0  -559.0\n",
       "1986-01-01   2859  7.958227   -56.0   2282.0  -161.0\n",
       "1987-01-01   3184  8.065894   325.0   2445.0   163.0\n",
       "1988-01-01   3721  8.221748   537.0   2800.0   355.0\n",
       "1989-01-01   4322  8.371474   601.0   3008.0   208.0\n",
       "1990-01-01   3887  8.265393  -435.0   2363.0  -645.0\n",
       "1991-01-01   4683  8.451694   796.0   2025.0  -338.0\n",
       "1992-01-01   5073  8.531688   390.0   2410.0   385.0\n",
       "1994-01-01   3458  8.148446 -1615.0    873.0 -1537.0\n",
       "1995-01-01   3081  8.033009  -377.0    537.0  -336.0\n",
       "1996-01-01   3056  8.024862   -25.0    186.0  -351.0\n",
       "1997-01-01   3200  8.070906   144.0   -294.0  -480.0\n",
       "1998-01-01    933  6.838405 -2267.0  -1982.0 -1688.0\n",
       "1999-01-01   1396  7.241366   463.0  -1463.0   519.0\n",
       "2000-01-01   1813  7.502738   417.0  -1371.0    92.0\n",
       "2001-01-01   1908  7.553811    95.0  -1813.0  -442.0\n",
       "2002-01-01   1332  7.194437  -576.0  -2990.0 -1177.0\n",
       "2003-01-01   1262  7.140453   -70.0  -2625.0   365.0\n",
       "2004-01-01   1161  7.057037  -101.0  -3522.0  -897.0\n",
       "2005-01-01   2011  7.606387   850.0  -3062.0   460.0\n",
       "2006-01-01   2751  7.919720   740.0   -707.0  2355.0\n",
       "2007-01-01   3241  8.083637   490.0    160.0   867.0\n",
       "2008-01-01   4787  8.473659  1546.0   1731.0  1571.0\n",
       "2009-01-01   4721  8.459776   -66.0   1521.0  -210.0\n",
       "2010-01-01   4821  8.480737   100.0   3888.0  2367.0\n",
       "2011-01-01   5067  8.530504   246.0   3671.0  -217.0\n",
       "2012-01-01   8498  9.047586  3431.0   6685.0  3014.0\n",
       "2013-01-01  11990  9.391828  3492.0  10082.0  3397.0\n",
       "2014-01-01  16840  9.731512  4850.0  15508.0  5426.0\n",
       "2015-01-01  14806  9.602788 -2034.0  13544.0 -1964.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------Selecting the Transformation----------------------------------------------\n",
    "\n",
    "As you can see by the p-value, taking the first difference is the best option w.r.t. other transformation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---------------------------------Plot the ACF and PACF charts-----------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "value_fd=data.fd.dropna(inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIkKRODbkmSJEmSMpmSgdSkHLZv38KDD97HqaeexWmnLZvu4kiSJEnSGAbd\najsDA7tZs2YlO3YsYe/eczn++FtYsmQHa9duYMGCRdNdPEmSJEk6zKBbbWfNmpVs3XoV8FwA+vuh\nv38za9asZP36G6e1bJIkSZJUzT7daivbt29hx44lVALuEc9lx47FbN++ZTqKJUmSJEl1GXSrrTz4\n4H3s3Xtu3W17957HQw/dP8UlkiRJkqTGDLrVVk499SyOP/6uutuOP/5OTjnlzCku0cS2b9/Cd77z\nZWvhJUmSpA5kn26N0cqjgp922jKWLNlBf/9mRjcx38ySJTtbqrwO+CZJkiTJoFuHtUuQuHbtBtas\nWcmDDz6NPXueywknbObUUx9l7doN0120URzwTZIkSZJBtw5rlyBxwYJFrF9/I9/61t/ygQ/8Jlde\n+SVe/vI3THexRpnMgG+tVCsvSc3Syq2lJEmaDgbdAtozSDz55DNGLVvJZAZ8a7X3E/yxLOnotUtr\nKUmSpppBt4D2DRJbVTHg2y3094/dVgz4dvnUF2oc/liWdKzapbWUJElTzdHLBbTnqOCtrDLgG2yu\n2dJ6A77ByI/l/v5PMjT0Vvr7P8nWrVexZs3K6S6apDYwmdZSkiR1KoNuAe0XJObSzOm91q7dwNKl\nV3HCCW8FPsEJJ7yVpUuvarkB3/yxLOlYTaa1VKdwmkipObyWNJPYvFyHtcuo4DnkaF6de8C3ZvW/\ntmuBpGPVbl1qcrCbjtQcXkuaiQy6dVg7jAqeS86+iM0e8K3ZX0b+WJZ0rCqtpfr7NzO61UzntJay\nT7vUHF5LmolsXq4xWnlU8BzarXl1s/tf27VAUjO0S5eaHNrte0RqVV5LmqkMutXx2qkvYq4vo07+\nsSzZb7A5Kq2lrrzyQuAKrrzyQtavv7EjmoO20/eI1Mq8ljRT2bxcHa+dmlfn6n/dyV0L1LnsN5hH\np7WWgvb6HlGhWeOiqLm8ljRTGXSr47VTX8TcX0ad+GO53fhDsXnsN6hmaafvkU7nzbbW5rWkmcqg\nW6J9Rm73y6hz+UOxuSbTVcPrSUeiXb5HOp0321qf15JmIoNuifZqXu2XUWfyh2JzOVWemq2dvkc6\nlTfb8mlmKyyvJc1EBt1SlXZoXu2XUefxh2Lz2W9QubTD90in8mZb8+VsheW1pJlkSkYvj4h3RMS2\niNgfEbdHxPkT5H95RGyMiAMRcW9EvGkqyim1E7+MOoejuTafU+VJnae42XZX3W3FzbYzp7hE7a/Z\n05hKM1UZ/LISAAAgAElEQVT2oDsi3gh8DHg/cB7wQ+DmiKh7+ysiTgNuAr4BPA/4S+CvIuKXcpdV\nklqRPxTzcKo8qbN4s625nFNbmrypqOleDVybUvp8Sulu4G3APuDNDfK/HXggpfQ/U0r3pJQ+Cfxt\n+TiS1HH8oZhHJ88r3Y46eT71Tj73ZvNmW/P+n2yFVfD61GRk7dMdEbOAFcCfVtJSSikibgVe3GC3\nFwG31qTdDKzLUkhJWXXyFFfNPHcH0MvHrhqtrZNH7u/kc69o9ndIJ4+L0uz/p04fG8PrU0ci90Bq\ni4BuYFdN+i7gOQ32Wdwg/1MiYk5K6WC9nbZuPZZiTo2tW+cB55WXzXH//U8BzuOnP23eMYtjNfeY\nOeQoZ7scM9dxm3nMvXt3c801K9m9ewn79p3L/Pm3sGjRDi6/fAPHHz+zv4zynPsi3v3uG9m48ats\n2PBuenv/ghUrfo1du2BX7Semjki7fOa1i2a/nx/+8Eq2bbuK2pH7V69eyXvf23oj9zfz/Nvt3Jsp\n93fI0NB/As5jaOg/ce+9x17edtD8/6dlnHhi/WlMTzxxJ4cOLTum97bVP5s7+frM7dChYGDgeO66\nq4sTTpju0jR2JPFnpJSyFSQilgAPAS9OKd1Rlf4R4GUppTG13RFxD/CZlNJHqtJeTdHPe35t0B0R\ny4GN8DJgQc3RessPSVPv9cBV1H4RF2kz/cuok89daqYtwCfLj1qrgMuBmdqCppPPHfwcbbZc/0+7\ngZUUdWbnAXcCO4ENFHVvM1WnX5+dqK/8qDYA3AawIqW0aby9c9d07wZKwEk16SdRXJH17GyQ/4lG\ntdwAX/jCOpYuXX605WxbO3bs4N/+7RFOPvl5012Ucf30p1v50Icu4X3v+xue9ayl010cZfbww1u4\n+uol7NkzdnCVE05YzJVXbuHkk4/+yyjH/1Ozjpn73HPIdX228t8pt3Y591Z/P++66z42bDiXUmns\ntp6e81i58n6e97zW+ixplnY8907+HIXWfk/z/T8tAm7k4Ye38LOf3c/Tn355S/5tKlr//RzRyv9P\nuY/54x/fxUc/+mY2bPg0K1asaMoxj93YytytWzdx6aWTK1/WoDulNBgRG4ELgK8ARESUn3+8wW7f\nA15dk/aqcnpDS5fC8s6LufnJTw7xyCNPcvrp012SiewH7uRZz9rP2WdPd1mU2yOP3Mf+/fUHV9m/\n/zxmzbqfs88+li+jHP9PzTlm/nPPIdf12bp/p/za5dxb+/2cPfss+voa9xk9//zLOe20oz/+ww//\nO3AnPT3/ztlnt1bQnfvcW/n/qT0/R6GV39Pc/0/F36MV/ya12uP9hFyfT637P1rtJz/5EXAns2f/\niOXLWyXoPjZTMXr51cBbI+K3I+Ic4FPAfOA6gIhYGxGfq8r/KeCMiPhIRDwnIlYBbygfR1Ib6OQp\nrjr53KVmyzVy/8DAblatej1XX30L8AmuvvoWVq16PQMDu4+1yE3TybMW+DnafJ38/5RDzvcz5+fT\nww8/MGrZairn/vGP/wvwCd797u/wohe9nt27W+ez+Wjlbl5OSumG8pzcH6RoJn4XcGFK6WflLIuB\nZ1bl3x4Rv0oxWvkVwIPAW1JKtSOaS2pRlS+jeoOrzPQv904+dymHysj9O3YsZu/e8zj++DtZsmTn\nMY3cv2bNSrZuvYrKNbpnD2zdupk1a1ayfn3r9BfOce7twM/RPDr1/ymXXO9njs+nykjrDz64iEog\nf8MN1zdlpPXqQP7ss4+t2XHtuff3wx13bOaii1Zy++2t89l8NLIH3QAppfXA+gbbLquTdhvFVGOS\n2lQnf7l38rlLzVaZ4mn79i089ND9nHLK5ccUdG3fvoUdO5YwOpgDeC47dixm+/YtLRPUNfvc24mf\no83Xyf9PFc0MEHO8n7k+n9ohkB/v3LdtW8yWLVtYtqx9/1+nJOiW1Hna7cu91b+IpU532mnLmnId\nPfjgfezdW7+/8N695/HQQ/e33PXarHPPrdM/R5t5/jm1y/9TM+Ws6W3m+5nj86ldAvnxzr2//zzu\nv//+tg66p6JPt6QOdtppy3jJS17Tsl/wOftOtfq5S53I/sLN1+mfo+0wRkCnqwSIe/ZsAC5nz54N\nbN16FWvWrJzuoo2S4/NpMoH8kZpMIH+kxjv3hQvv5Mwz2/uz2aBbUkdrly9iSc3hgFLN1+mfo51+\n/q0uR4CYS47Pp3YJ5Mc79zPO2NnWtdxg0C2pg7XTF7Gk5lm7dgNLl17FwoWr6On5NAsXrmLp0qvs\nL3wUOv1zNPf5t/po0+0gR4CYU7M/n9olkIfqc387PT3X8vSnv40XvegqvvrV9v9stk+3pI7Vjn07\npWZqlz6ozdaO/YWbrVl/+07/HM11/jn7IHeaIkBsPKf2KadcPvWFGkeOz6dmD0yYa4aByrnfc89G\n7r336/y3/3YZL3zhC4/qWK3GoFtSx2q3L2KpWfxBX3BAqWP/23f652iu82+Xae3aQbtOQdfMz6d2\nCOSrPfvZSznhhD2cc845x3ysVmHQLaljtesXsXSs/EHfuZr9t+/0z9Ec599O09q1C6egK7R6ID+T\nGXRL6mh+EavT+IO+c+X623f652izz7/Tm+znYICYTye2GDoaBt2SOppfxOo0/qDvXLn+9p3+Odrs\n8+/0Jvs5GSBquhh0a0p06mA9ah9+ETef131r8gd958r9t+/0z9FmnX+nN9mXZiKnDFNWAwO7WbXq\n9Vx99S1UBmxZter1DAzsnu6iScrE6761OU915/Jv3z6c1k6aWazpVlYO1iN1Hq/71tfpfXA7mX/7\n9tDpTfalmcagW9k4WI/Uebzu24M/6DuXf/v20ulN9qWZwqBb2ThYj9R5vO7zydFH3h/0ncu/vSRN\nHft0K5tiwJa76m4rBmw5c4pLJCk3r/vms4+8JEntzaBb2Thgi9R5vO6br9JHfs+eDcDl7Nmzga1b\nr2LNmpXTXTRJkjQJNi9XVg7YInUer/vmsY+8JEntz6BbWTlgi9R5vO6bxz7ykiS1P4NuTQkHbJE6\nj9f9sSv6yN9Cf//YbUUf+cunvlCSJOmI2KdbUtupHsVZmsnsIy9JUvsz6JbUNhzFOQ9vYrS2tWs3\nsHTpVSxcuIqenk+zcOEqli69yj7ykiS1CZuXS2oblVGcK4NK7dkDW7duZs2alaxff+O0lq0dDQzs\nZs2alTz44CIqNzFuuOF61q7dwIIFi6a7eG2tmXNq20dekqT2ZtAtqS04inPzeROj+XLeyLCPvCRJ\n7cnm5ZLawmRGcdbkTeYmRitq9abwzqktSZJqGXRLagvFKM531d1WjOJ85hSXqL21202MdujP3643\nMiRJUl4G3ZLagqM4N1e73cRohxrkdruRIUmSpka2oDsiToyIv4mIgYh4PCL+KiKOGyd/T0R8JCI2\nR8TeiHgoIj4XEUtylVFSe3EU5+Zpp5sY7VKD3G43MiRJ0tTIOZDa9cBJwAXAbOA64Frg0gb55wPn\nAh+g+BV4IvBx4MvACzKWU1KbcBTn5lq7dgNr1qxkx47F7N17HscffydLluxsuZsYk6lBboX/g8qN\njP7+zYy+QdB6NzIkSdLUyRJ0R8Q5wIXAipTSneW0dwJfi4j3pJR21u6TUnqivE/1cS4H7oiIU1NK\nD+Yoq6T24yjOzdEuNzGKGuRb6O8fu62oQb586gvVQLvcyJAkSVMnV033i4HHKwF32a1AAl5IUXs9\nGQvL+9T5qSVJaoZWv4nRTjXI7XIjQ5IkTZ1cQfdi4JHqhJRSKSIeK2+bUETMAT4MXJ9S2tv8IkqS\n2kW71SC3+o0MSZI0dY4o6I6ItcAfjJMlAUuPqUTF6/QAXyofb9WxHk+S1N6sQZYkSe3qSGu6/xz4\n7AR5HgB2As+oToyIbuCp5W0NVQXczwR+cbK13KtXr2bBggWj0np7e+nt7Z3M7pKkNmANsiRJmmp9\nfX309fWNShsYGJj0/kcUdKeUHgUenShfRHwPWBgR51X1674ACOCOcfarBNxnAK9IKT0+2bKtW7eO\n5cuXTza7JEmSJEkTqleZu2nTJlasWDGp/bPM051Suhu4Gfh0RJwfES8BPgH0VY9cHhF3R8Rry+s9\nwN8ByymmFZsVESeVH7NylFOSJEmSpJxyztN9MXANxajlw8DfAu+qyXMWUGkTfgpwUXn9rvIyKPp1\nvwK4LWNZJUmSJElqumxBd0qpn6LGerw83VXrPwG6x8kuSZIkSVJbydK8XJIkSZIkGXRLkiRJkpSN\nQbckSZIkSZkYdEuSJEmSlIlBtyRJkiRJmRh0S5IkSZKUiUG3JEmSJEmZGHRLkiRJkpSJQbckSZIk\nSZkYdEuSJEmSlIlBtyRJkiRJmRh0S5IkSZKUiUG3JEmSJEmZ9Ex3AXRsurq6mD37EA88cDswj9mz\n5zNnzjxmz57H3LnzmT17LhEx3cWUJEmSpI5k0N3mlixZwgUXzGLfvn3s37+fxx/vZ2BgB/v3D7N3\nLxw6FKQ0j4giEJ8zpwjK58yZz6xZsw3IJUmSJCkjg+4219PTw+LFi0elpZQ4ePAg+/fvZ//+/ezb\nt499+/bz+OOPMjDwEAcOJPr7YXCwi5Tm0dVVBOJFzfg85syZx6xZs6fpjCRJkiRp5jDonoEigrlz\n5zJ37lxOPPHEUdtSShw4cOBwzfi+fft48sn9PP74Lp544iBPPgmPPQZDQ11EdJNSAEHR/X9kGdFF\nStXLYr162dVV5O3q6irXqFfW620fSat3nPrHliRJkqTWZtDdYSKCefPmMW/evDHbSqXS4YD84MGD\nDA8Pk1JquKxeL5WGKZVKlErDDA8nhofT4fViW5G3shwaGiYlSAmGh0cva9MbpY0E/N2k1EVxQ6Ab\nKG4YFMsuurq66eoaWU6UVqx3HX6/yu/c4fUi+B9Jr02zyb4kSZKkCoNuHdbd3c1xxx3HcccdNyWv\nVxvI1wvuJ7O98iiVSjXLYQYHDzE0NMzgYKn8fJihoRJDQ8PlmwIwNFQE87XBf1HG+svKeqP0QhGI\nF8+Dei0EGrUiqCwnajlQP9gfG/xPlGds3pFjV55XtlWnNXpe7zg5eINDkiRJrc6gW9MmIuju7p62\n168O3GsD9kpNfiVfZdlofbJpE7UeGFkmSqXBUS0E6rUcGB5OVYF/9WuOXo4+77HLejcNam8qTPR8\non3y6CalbiJ6Di+hm+7uHrq6RpaV9e7usduKpd0VJEmSlIdBtzpWJejv7u5m1qxZ012cKVHvpkD1\nsrLe6DHR9vHy5jiXoktDiaGhocPLoaESBw/u59ChEocODR1eDg0lhobg4MGiNUOpNPIoWh8UgXt1\nrX7RjSEO3zioTZtoW1HO/DXyjd7e6tetl6e2XJVuFsUNisY3KUbWp++mmSRJUrsw6JY6SCf3Oy/G\nEhgaE6RXL0ul0pibBI1uTBzNtqkw2dcr8o3OOzQ0OOaGRdE1AwYHR25SVG5aDA8HxTgKPaNaG0RU\nB+djB0JstF7pQjH+QIwxatwFSZKkVmfQLakjdHV1MXu2U+EdqZRS3ZsU46UdPHiIQ4f2c+jQUDlo\nT+UBFouuEZWuD/XGUai3Xn9AxS4iZpFSD8VXWQ89PbOqaumr10dvM2CXJElTyaBbktRQRDBr1qym\ndsGo7nZQbzaEidKqg/yhoSEGBwcZHBzi4MH9HDgwxMGDgxw8OMShQ8MMDsKBA8WAiZWa+mIsgJFg\nPWIWXV3ddVqAjG0RUq+VyHhpo5vr9zR4bjN9SZJmMoNuSdKUqh7RPmfAOTw8zODg4JgAvXa9CNgP\n1PR7r3QPqEpJjdOq0ytpw8OJwcESBw8OTaKZ/kgT/cp6o4C9coOgeorDRktJkjT9DLolSTNSV1cX\nc+bMYc6cOdNdlDE19I3WK88rNwKKGvuij/3Bg0OUSiPTHNZroj+2yX7X4UcxHeHIo5i2sLJtZIo/\nGH1j5MjS608vWC/tSKYcrD3OVJjodcbfXn9axommbezE8TYkqRNkC7oj4kTgGuAiYBj4O+BdKaUn\nJ7n/p4CVwLtTSh/PVU5JknKLCHp6eujpOfqv3UrgXpnqsPpRPQXi0T4qUwwW/e8r0xGOrFe2jUxJ\nODa9fv6RYxbnMfZRnV6bp3ZbLsdy/Hr71k6pWC+tdr96syPUmy2h+aLq+DHm+XjrMHZb7SwOldeA\n0Wn1bzZMtD2Pxq9RP30yXU0mOq/x1hu9D0d3M2xseqNzaE3HehOr8Xm3z3ugdpezpvt64CTgAmA2\ncB1wLXDpRDtGxOuAFwIPZSyfJEltoxK4t7NWnH6wUTmbsX285WTy1Fs2W6P3+EjW620beT66a0Yl\nrXb7yG5j00Zvb77a97a2q0ij/ON1P6l3TrXpte9LvWPVew9rl43Wq8+n0Y2eqXCsrzneTapj2V6/\nfKMD8qPZllP1dKTN3D5Reu22idIrafWOW/ve1XteKpU4/vj659Cusnx7R8Q5wIXAipTSneW0dwJf\ni4j3pJR2jrPvKcBflvf/hxzlkyRJU290zZukI3U0N0bG295qJr7R0dxlo9etdxNkss9zONYbgePl\nyZV+rM+7uo7nuOOOq/ta7SjXLfMXA49XAu6yW4FEUYP95Xo7RfFN/HngoymlrX4xS5IkSQVvXEnt\nKdfQpouBR6oTUkol4LHytkbeCxxKKV2TqVySJEmSJE2ZIwq6I2JtRAyP8yhFxNlHU5CIWAFcAVx2\nNPtLkiRJktRqjrR5+Z8Dn50gzwPATuAZ1YkR0Q08tbytnpcCTwf+o6rZTDdwdUS8O6V0xngvunr1\nahYsWDAqrbe3l97e3gmKK0mSJElSfX19ffT19Y1KGxgYmPT+kaPzf3kgtX8Hnl81kNqrKAZGO7Xe\nQGrlKcaW1CTfQtHH+7MppfsavNZyYOPGjRtZvnx5E89CkiRJkqSxNm3axIoVK6AYPHzTeHmz9OlO\nKd0N3Ax8OiLOj4iXAJ8A+qoD7oi4OyJeW97n8ZTSluoHMAjsbBRwt6PaOySSjo7XktQcXktS83g9\nSc0x066lXAOpAVwM3E0xavlNwG3A79XkOQtYQGOtOZ/BMZhp/0DSdPFakprDa0lqHq8nqTlm2rWU\na8owUkr9wKUT5OmeYPu4/bglSZIkSWplOWu6JUmSJEnqaAbdkiRJkiRlkq15+RSaC7B169bpLsek\nDAwMsGnTuIPbSZoEryWpObyWpObxepKaox2upar4c+5EebNMGTaVIuJi4G+muxySJEmSpI5zSUrp\n+vEyzISg+2nAhcB24MD0lkaSJEmS1AHmAqcBN6eUHh0vY9sH3ZIkSZIktSoHUpMkSZIkKRODbkmS\nJEmSMjHoliRJkiQpE4NuSZIkSZIyMeieIhHxjojYFhH7I+L2iDh/ussktbqI+PmI+EpEPBQRwxHx\nmjp5PhgRD0fEvoj4p4g4czrKKrWqiFgTEd+PiCciYldE/L+IOLtOPq8laQIR8baI+GFEDJQf342I\nX67J47UkHaGIeG/5t97VNekz4noy6J4CEfFG4GPA+4HzgB8CN0fEomktmNT6jgPuAlYBY6ZaiIg/\nAC4HVgIvAJ6kuLZmT2UhpRb388AngBcCrwRmAbdExLxKBq8ladL+A/gDYDmwAvhn4MsRsRS8lqSj\nUa6MXEkRI1Wnz5jrySnDpkBE3A7ckVJ6V/l5UHxofzyl9NFpLZzUJiJiGPj1lNJXqtIeBv4spbSu\n/PwpwC7gTSmlG6anpFJrK9/wfQR4WUrpO+U0ryXpKEXEo8B7Ukqf9VqSjkxEHA9sBN4O/BFwZ0rp\nyvK2GXM9WdOdWUTMorgT+o1KWirudNwKvHi6yiW1u4g4HVjM6GvrCeAOvLak8SykaDnyGHgtSUcr\nIroi4reA+cB3vZako/JJ4KsppX+uTpxp11PPdBegAywCuinuylTbBTxn6osjzRiLKQKHetfW4qkv\njtT6yi2t/gL4TkppSznZa0k6AhHxn4HvAXOBPcDrUkr3RMSL8VqSJq180+pc4Pl1Ns+o7yaDbkmS\nOsd6YBnwkukuiNTG7gaeBywA3gB8PiJeNr1FktpLRJxKcRP4lSmlwekuT242L89vN1ACTqpJPwnY\nOfXFkWaMnUDgtSVNSkRcA/wK8PKU0o6qTV5L0hFIKQ2llB5IKd2ZUnofxeBP78JrSToSK4CnA5si\nYjAiBoFfAN4VEYcoarRnzPVk0J1Z+c7NRuCCSlq5ed8FwHenq1xSu0spbaP40K2+tp5CMUKz15ZU\npRxwvxZ4RUrpp9XbvJakY9YFzPFako7IrcB/oWhe/rzy4wfAF4DnpZQeYAZdTzYvnxpXA9dFxEbg\n+8BqikE3rpvOQkmtLiKOA86kuNMJcEZEPA94LKX0HxTNkv4wIu4HtgP/G3gQ+PI0FFdqSRGxHugF\nXgM8GRGVWoOBlNKB8rrXkjQJEfGnwD8CPwVOAC6hqJ17VTmL15I0CSmlJ4Et1WkR8STwaEppazlp\nxlxPBt1TIKV0Q3mKlg9SNIm4C7gwpfS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kSZkYuiVJkiRJysTQLUmSJElSJoZuSZIkSZIy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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abed9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm \n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(value_fd.iloc[1:], lags=40, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(value_fd.iloc[1:], lags=40, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------Interpretation of Plots-------------------------------\n",
    "From the ACF plot we can see that it decays after 1 hence p=1 and from pacf plots we can see that that there is no significant peak, so q=0. Hence, the parametrs for our ARIMA models p=1,q=0 and d=1<-as we found that series is stationary at first difference.\n",
    "----------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1970-01-01       NaN\n",
       "1971-01-01    -181.0\n",
       "1972-01-01      22.0\n",
       "1973-01-01     -20.0\n",
       "1974-01-01     105.0\n",
       "1975-01-01     162.0\n",
       "1976-01-01     182.0\n",
       "1977-01-01     393.0\n",
       "1978-01-01     210.0\n",
       "1979-01-01    1134.0\n",
       "1980-01-01       5.0\n",
       "1981-01-01     -78.0\n",
       "1982-01-01     -41.0\n",
       "1983-01-01     326.0\n",
       "1984-01-01     624.0\n",
       "1985-01-01    -579.0\n",
       "1986-01-01     -56.0\n",
       "1987-01-01     325.0\n",
       "1988-01-01     537.0\n",
       "1989-01-01     601.0\n",
       "1990-01-01    -435.0\n",
       "1991-01-01     796.0\n",
       "1992-01-01     390.0\n",
       "1994-01-01   -1615.0\n",
       "1995-01-01    -377.0\n",
       "1996-01-01     -25.0\n",
       "1997-01-01     144.0\n",
       "1998-01-01   -2267.0\n",
       "1999-01-01     463.0\n",
       "2000-01-01     417.0\n",
       "2001-01-01      95.0\n",
       "2002-01-01    -576.0\n",
       "2003-01-01     -70.0\n",
       "2004-01-01    -101.0\n",
       "2005-01-01     850.0\n",
       "2006-01-01     740.0\n",
       "2007-01-01     490.0\n",
       "2008-01-01    1546.0\n",
       "2009-01-01     -66.0\n",
       "2010-01-01     100.0\n",
       "2011-01-01     246.0\n",
       "2012-01-01    3431.0\n",
       "2013-01-01    3492.0\n",
       "2014-01-01    4850.0\n",
       "2015-01-01   -2034.0\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I found that there is built in function for calcuating first difference.\n",
    "data['count'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 Statespace Model Results                                \n",
      "=========================================================================================\n",
      "Dep. Variable:                             count   No. Observations:                   45\n",
      "Model:             SARIMAX(1, 1, 0)x(1, 1, 1, 2)   Log Likelihood                -356.291\n",
      "Date:                           Mon, 24 Apr 2017   AIC                            720.583\n",
      "Time:                                   12:39:42   BIC                            727.809\n",
      "Sample:                               01-01-1970   HQIC                           723.277\n",
      "                                    - 01-01-2015                                         \n",
      "Covariance Type:                             opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1          0.2009      0.164      1.223      0.222      -0.121       0.523\n",
      "ar.S.L2       -0.5036      0.312     -1.614      0.106      -1.115       0.108\n",
      "ma.S.L2       -0.3536      0.272     -1.299      0.194      -0.887       0.180\n",
      "sigma2      1.358e+06   2.07e+05      6.551      0.000    9.52e+05    1.76e+06\n",
      "===================================================================================\n",
      "Ljung-Box (Q):                       20.26   Jarque-Bera (JB):                10.77\n",
      "Prob(Q):                              1.00   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):              14.16   Skew:                            -0.04\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.48\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(data['count'], trend='n', order=(1,1,0),seasonal_order=(1,1,1,2))\n",
    "results = mod.fit()\n",
    "print results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['forecast'] = results.predict(start = 25, end= 32, dynamic= True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>log</th>\n",
       "      <th>fd</th>\n",
       "      <th>sd</th>\n",
       "      <th>sfd</th>\n",
       "      <th>forecast</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970-01-01</th>\n",
       "      <td>651</td>\n",
       "      <td>6.478510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971-01-01</th>\n",
       "      <td>470</td>\n",
       "      <td>6.152733</td>\n",
       "      <td>-181.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972-01-01</th>\n",
       "      <td>492</td>\n",
       "      <td>6.198479</td>\n",
       "      <td>22.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973-01-01</th>\n",
       "      <td>472</td>\n",
       "      <td>6.156979</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974-01-01</th>\n",
       "      <td>577</td>\n",
       "      <td>6.357842</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01</th>\n",
       "      <td>739</td>\n",
       "      <td>6.605298</td>\n",
       "      <td>162.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976-01-01</th>\n",
       "      <td>921</td>\n",
       "      <td>6.825460</td>\n",
       "      <td>182.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977-01-01</th>\n",
       "      <td>1314</td>\n",
       "      <td>7.180831</td>\n",
       "      <td>393.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978-01-01</th>\n",
       "      <td>1524</td>\n",
       "      <td>7.329094</td>\n",
       "      <td>210.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979-01-01</th>\n",
       "      <td>2658</td>\n",
       "      <td>7.885329</td>\n",
       "      <td>1134.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-01-01</th>\n",
       "      <td>2663</td>\n",
       "      <td>7.887209</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981-01-01</th>\n",
       "      <td>2585</td>\n",
       "      <td>7.857481</td>\n",
       "      <td>-78.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982-01-01</th>\n",
       "      <td>2544</td>\n",
       "      <td>7.841493</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>1893.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983-01-01</th>\n",
       "      <td>2870</td>\n",
       "      <td>7.962067</td>\n",
       "      <td>326.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984-01-01</th>\n",
       "      <td>3494</td>\n",
       "      <td>8.158802</td>\n",
       "      <td>624.0</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>602.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985-01-01</th>\n",
       "      <td>2915</td>\n",
       "      <td>7.977625</td>\n",
       "      <td>-579.0</td>\n",
       "      <td>2443.0</td>\n",
       "      <td>-559.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-01</th>\n",
       "      <td>2859</td>\n",
       "      <td>7.958227</td>\n",
       "      <td>-56.0</td>\n",
       "      <td>2282.0</td>\n",
       "      <td>-161.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-01-01</th>\n",
       "      <td>3184</td>\n",
       "      <td>8.065894</td>\n",
       "      <td>325.0</td>\n",
       "      <td>2445.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>3721</td>\n",
       "      <td>8.221748</td>\n",
       "      <td>537.0</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989-01-01</th>\n",
       "      <td>4322</td>\n",
       "      <td>8.371474</td>\n",
       "      <td>601.0</td>\n",
       "      <td>3008.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>3887</td>\n",
       "      <td>8.265393</td>\n",
       "      <td>-435.0</td>\n",
       "      <td>2363.0</td>\n",
       "      <td>-645.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-01-01</th>\n",
       "      <td>4683</td>\n",
       "      <td>8.451694</td>\n",
       "      <td>796.0</td>\n",
       "      <td>2025.0</td>\n",
       "      <td>-338.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-01-01</th>\n",
       "      <td>5073</td>\n",
       "      <td>8.531688</td>\n",
       "      <td>390.0</td>\n",
       "      <td>2410.0</td>\n",
       "      <td>385.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-01-01</th>\n",
       "      <td>3458</td>\n",
       "      <td>8.148446</td>\n",
       "      <td>-1615.0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>-1537.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-01-01</th>\n",
       "      <td>3081</td>\n",
       "      <td>8.033009</td>\n",
       "      <td>-377.0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>-336.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-01</th>\n",
       "      <td>3056</td>\n",
       "      <td>8.024862</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>-351.0</td>\n",
       "      <td>3359.463000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-01-01</th>\n",
       "      <td>3200</td>\n",
       "      <td>8.070906</td>\n",
       "      <td>144.0</td>\n",
       "      <td>-294.0</td>\n",
       "      <td>-480.0</td>\n",
       "      <td>3465.260109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-01-01</th>\n",
       "      <td>933</td>\n",
       "      <td>6.838405</td>\n",
       "      <td>-2267.0</td>\n",
       "      <td>-1982.0</td>\n",
       "      <td>-1688.0</td>\n",
       "      <td>2809.646467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-01-01</th>\n",
       "      <td>1396</td>\n",
       "      <td>7.241366</td>\n",
       "      <td>463.0</td>\n",
       "      <td>-1463.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>2676.221892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>1813</td>\n",
       "      <td>7.502738</td>\n",
       "      <td>417.0</td>\n",
       "      <td>-1371.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2491.754285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-01-01</th>\n",
       "      <td>1908</td>\n",
       "      <td>7.553811</td>\n",
       "      <td>95.0</td>\n",
       "      <td>-1813.0</td>\n",
       "      <td>-442.0</td>\n",
       "      <td>2478.949326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>1332</td>\n",
       "      <td>7.194437</td>\n",
       "      <td>-576.0</td>\n",
       "      <td>-2990.0</td>\n",
       "      <td>-1177.0</td>\n",
       "      <td>2057.263004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>1262</td>\n",
       "      <td>7.140453</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>-2625.0</td>\n",
       "      <td>365.0</td>\n",
       "      <td>1983.725176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-01-01</th>\n",
       "      <td>1161</td>\n",
       "      <td>7.057037</td>\n",
       "      <td>-101.0</td>\n",
       "      <td>-3522.0</td>\n",
       "      <td>-897.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-01-01</th>\n",
       "      <td>2011</td>\n",
       "      <td>7.606387</td>\n",
       "      <td>850.0</td>\n",
       "      <td>-3062.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-01-01</th>\n",
       "      <td>2751</td>\n",
       "      <td>7.919720</td>\n",
       "      <td>740.0</td>\n",
       "      <td>-707.0</td>\n",
       "      <td>2355.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01</th>\n",
       "      <td>3241</td>\n",
       "      <td>8.083637</td>\n",
       "      <td>490.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-01</th>\n",
       "      <td>4787</td>\n",
       "      <td>8.473659</td>\n",
       "      <td>1546.0</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>1571.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-01-01</th>\n",
       "      <td>4721</td>\n",
       "      <td>8.459776</td>\n",
       "      <td>-66.0</td>\n",
       "      <td>1521.0</td>\n",
       "      <td>-210.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-01</th>\n",
       "      <td>4821</td>\n",
       "      <td>8.480737</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3888.0</td>\n",
       "      <td>2367.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>5067</td>\n",
       "      <td>8.530504</td>\n",
       "      <td>246.0</td>\n",
       "      <td>3671.0</td>\n",
       "      <td>-217.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>8498</td>\n",
       "      <td>9.047586</td>\n",
       "      <td>3431.0</td>\n",
       "      <td>6685.0</td>\n",
       "      <td>3014.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>11990</td>\n",
       "      <td>9.391828</td>\n",
       "      <td>3492.0</td>\n",
       "      <td>10082.0</td>\n",
       "      <td>3397.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>16840</td>\n",
       "      <td>9.731512</td>\n",
       "      <td>4850.0</td>\n",
       "      <td>15508.0</td>\n",
       "      <td>5426.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>14806</td>\n",
       "      <td>9.602788</td>\n",
       "      <td>-2034.0</td>\n",
       "      <td>13544.0</td>\n",
       "      <td>-1964.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count       log      fd       sd     sfd     forecast\n",
       "year                                                             \n",
       "1970-01-01    651  6.478510     NaN      NaN     NaN          NaN\n",
       "1971-01-01    470  6.152733  -181.0      NaN     NaN          NaN\n",
       "1972-01-01    492  6.198479    22.0      NaN     NaN          NaN\n",
       "1973-01-01    472  6.156979   -20.0      NaN     NaN          NaN\n",
       "1974-01-01    577  6.357842   105.0      NaN     NaN          NaN\n",
       "1975-01-01    739  6.605298   162.0      NaN     NaN          NaN\n",
       "1976-01-01    921  6.825460   182.0      NaN     NaN          NaN\n",
       "1977-01-01   1314  7.180831   393.0      NaN     NaN          NaN\n",
       "1978-01-01   1524  7.329094   210.0      NaN     NaN          NaN\n",
       "1979-01-01   2658  7.885329  1134.0      NaN     NaN          NaN\n",
       "1980-01-01   2663  7.887209     5.0      NaN     NaN          NaN\n",
       "1981-01-01   2585  7.857481   -78.0      NaN     NaN          NaN\n",
       "1982-01-01   2544  7.841493   -41.0   1893.0     NaN          NaN\n",
       "1983-01-01   2870  7.962067   326.0   2400.0   507.0          NaN\n",
       "1984-01-01   3494  8.158802   624.0   3002.0   602.0          NaN\n",
       "1985-01-01   2915  7.977625  -579.0   2443.0  -559.0          NaN\n",
       "1986-01-01   2859  7.958227   -56.0   2282.0  -161.0          NaN\n",
       "1987-01-01   3184  8.065894   325.0   2445.0   163.0          NaN\n",
       "1988-01-01   3721  8.221748   537.0   2800.0   355.0          NaN\n",
       "1989-01-01   4322  8.371474   601.0   3008.0   208.0          NaN\n",
       "1990-01-01   3887  8.265393  -435.0   2363.0  -645.0          NaN\n",
       "1991-01-01   4683  8.451694   796.0   2025.0  -338.0          NaN\n",
       "1992-01-01   5073  8.531688   390.0   2410.0   385.0          NaN\n",
       "1994-01-01   3458  8.148446 -1615.0    873.0 -1537.0          NaN\n",
       "1995-01-01   3081  8.033009  -377.0    537.0  -336.0          NaN\n",
       "1996-01-01   3056  8.024862   -25.0    186.0  -351.0  3359.463000\n",
       "1997-01-01   3200  8.070906   144.0   -294.0  -480.0  3465.260109\n",
       "1998-01-01    933  6.838405 -2267.0  -1982.0 -1688.0  2809.646467\n",
       "1999-01-01   1396  7.241366   463.0  -1463.0   519.0  2676.221892\n",
       "2000-01-01   1813  7.502738   417.0  -1371.0    92.0  2491.754285\n",
       "2001-01-01   1908  7.553811    95.0  -1813.0  -442.0  2478.949326\n",
       "2002-01-01   1332  7.194437  -576.0  -2990.0 -1177.0  2057.263004\n",
       "2003-01-01   1262  7.140453   -70.0  -2625.0   365.0  1983.725176\n",
       "2004-01-01   1161  7.057037  -101.0  -3522.0  -897.0          NaN\n",
       "2005-01-01   2011  7.606387   850.0  -3062.0   460.0          NaN\n",
       "2006-01-01   2751  7.919720   740.0   -707.0  2355.0          NaN\n",
       "2007-01-01   3241  8.083637   490.0    160.0   867.0          NaN\n",
       "2008-01-01   4787  8.473659  1546.0   1731.0  1571.0          NaN\n",
       "2009-01-01   4721  8.459776   -66.0   1521.0  -210.0          NaN\n",
       "2010-01-01   4821  8.480737   100.0   3888.0  2367.0          NaN\n",
       "2011-01-01   5067  8.530504   246.0   3671.0  -217.0          NaN\n",
       "2012-01-01   8498  9.047586  3431.0   6685.0  3014.0          NaN\n",
       "2013-01-01  11990  9.391828  3492.0  10082.0  3397.0          NaN\n",
       "2014-01-01  16840  9.731512  4850.0  15508.0  5426.0          NaN\n",
       "2015-01-01  14806  9.602788 -2034.0  13544.0 -1964.0          NaN"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x124d55550>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HnAOcC3y76bckSZIkSeqMOko4797UA1JKDwMPA0REbK8mIvYD/hE4mSxAl+7r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fc98610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['count', 'forecast']].plot(figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@Author:- Rajat Handa"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
